چکیده انگلیسی

Objectives
Research examining how emotional intelligence (EI) relates to the performance of athletes has been conducted using various EI measures including the Bar-On Emotional Quotient Inventory (EQ-i; Bar-On, 1997), but no one has investigated the factor structure of the EQ-i in the area of sport psychology. The current study explored the dimensional structure and factorial validity of the EQ-i in a sample of male athletes.
Design
Confirmatory factor analysis was used to examine Bar-On, 1997 and Bar-On, 2004 model of emotional–social intelligence and the 1-5-15 dimensional structure which underpins the EQ-i.
Method
A total sample of 706 male athletes from various sports and competing at the national age group level through to the professional level of competition completed the EQ-i.
Results
Confirmatory factor analyses demonstrated that the 1-5-15 dimensional structure was a poor fit for the data. A re-specification of the model representing the best fit for the data was a 1-4-15 dimensional structure. The factorial validity of the individual subscales was also examined at the item level using confirmatory factor analysis. Thirteen of the 15 subscales showed close, reasonably good, or mediocre fit for the data.
Conclusion
Further construct validation of Bar-On’s model and measure is required. Sport psychologists administering the EQ-i in applied practice should consider using the EQ-i subscales rather than referring back to the 1-5-15 dimensional structure.

مقدمه انگلیسی

A small number of researchers have presented reasonable rationale and evidence for the construct of emotional intelligence (EI) in sport. How EI is relevant to performance in sport was initially presented by Meyer and Fletcher (2007). More recently, Stanimirovic and Hanrahan (2010) suggested the potential for EI measures to predict criteria important to professional sport. The relationship between EI and the performance of athletes has been empirically tested in a sample of male professional cricket players (Crombie, Lombard, & Noakes, 2009) and male professional hockey players (Perlini & Halverson, 2006). The results provided preliminary support for the use of EI in professional sport samples as both a positive and negative predictor of performance outcomes specific to the sport. The evidence for the EI–performance relationship in collegiate baseball was not significant (Zizzi, Deaner, & Hirschhorn, 2003). A recent intervention study conducted by Crombie, Lombard, and Noakes (2011) showed how EI can be enhanced in a sample of elite cricketers with systematic training and development compared to a control group.
If EI measures are to be used in applied research and professional practice with athletes and potentially coaches, independent studies to evaluate the psychometric rigour of EI measures in sport are necessary. Measures used to test the EI–performance relationship in sport included the EI Scale (EIS; Schutte et al., 1998), Mayer–Salovey–Caruso EI Test Version 2 (MSCEIT; Mayer, Salovey, & Caruso, 2002), and the Bar-On Emotional Quotient Inventory (EQ-i; Bar-On, 1997). To date, the only psychometric investigation of any EI measure in sport was conducted by Lane et al. (2009) using the EI Scale. The current study explored the dimensional structure and factorial validity of the EQ-i (Bar-On, 1997) in a sample of male athletes.
The EQ-i was developed by Bar-On (1997) to better understand and facilitate psychological wellbeing and is based on the model of emotional–social intelligence (Bar-On, 2006). The model is theoretically linked to Darwinian concepts that recognise the importance of emotional expression for survival and adaptation. Ultimately, being emotionally and socially intelligent means to effectively manage personal, social, and environmental change by realistically and flexibly coping with the immediate situation, solving problems, and making decisions (Bar-On, 2006). The five key components of the model include (a) the ability to recognise, understand, and express emotions and feelings; (b) the ability to understand how others feel and relate with them; (c) the ability to manage and control emotions; (d) the ability to manage change, adapt, and solve problems of a personal and interpersonal nature; and (e) the ability to generate positive affect and be self motivated. The Bar-On model provides the theoretical basis for the EQ-i and the EQ-i measures the five key components and the 15 related competencies and skills.
Based on the EQ-i technical manual (Bar-On, 1997) it is generally assumed that the dimensional structure of the EQ-i is 1-5-15 (a general latent factor or total EQ, five first-order latent factors or composite scales, and 15 observed variables or subscales; see Fig. 1). Ongoing research specifically evaluating the psychometric properties of the EQ-i is essential because it is already the most widely used measure of EI. Perlini and Halverson (2006) used the composite scales of the EQ-i and related the factors to various performance outcomes that may be considered representative of job performance and career success in the National Hockey League (NHL) such as total points scored in the participants’ careers to date, number of games played in the participants’ careers to date, draft ranking, and years since the draft. Intrapersonal management (defined as self-awareness and self expression), and general mood (defined as self-motivation) were significantly related to career NHL games played (β-values .29 and −.30, respectively) after number of years experience was accounted for in the analyses. The contribution of EQ-i factors to relevant criteria in professional sport is preliminary, but certainly, requires further investigation.The factorial validity of the EQ-i has been examined using confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Using general population samples, an EFA using varimax rotation was conducted by Bar-On (1997) to assess the factorial structure of the EQ-i. Thirteen factors with Eigen values greater than 1.0 emerged from the data. The results clearly did not justify the 15 primary scales that Bar-On (1997) included in the EQ-i. Nevertheless, CFA conducted by Bar-On suggested that the 15-factor model was a better fit than the 13-factor model. A second CFA removed items from five subscales. Bar-On (2004) later presented a 10-factor model with the five removed subscales acting as facilitators that positively influence emotionally and socially intelligent behaviour. A CFA to determine the revised model has not yet been published by Bar-On.
Palmer, Manocha, Gignac, and Stough (2003) showed that the 1-5-15 model was a reasonably good fitting model using a general population sample. EFA using principal axis factoring was then conducted and the scree test and parallel analysis suggested that six factors should be extracted from the data set. The results demonstrated the plausibility of a general factor of EI and six primary factors with 13 of the original EQ-i subscales retained. Livingstone and Day (2005) demonstrated that the proposed EQ-i five-factor model was a poor fit for the data using a military sample. EFA using principal components analyses was then conducted to examine the structure of the EQ-i. The scree plot indicated the presence of three factors. Arteche, Chamorro-Premuzic, Furnham, and Crump (2008) examined the EQ-i using a sample from a private organisation and the results revealed a non-fitting model. EFA using principal components analyses and Varimax rotation was performed to investigate a more appropriate structure of the EQ-i. Inspection of the scree plot suggested a four-factor structure, as opposed to the original five higher-order factors proposed by Bar-On (1997).
To date, CFA has failed to confirm the 1-5-15 multi-factor model that underpins the Bar-On EQ-i (see Fig. 1 for a conventional higher-order model of the EQ-i). However, none of these investigators have used direct hierarchical modelling (a.k.a., nested factor modelling), which is a multi-factor modelling technique that has been demonstrated to yield superior model fit in comparison to the more conventional higher-order modelling strategy. When using conventional higher-order model solutions, a Schmid–Leiman transformation (Schmid & Leiman, 1957) should be applied because it provides a less ambiguous interpretation of the nature and strength of both the general factor and group level covariance. A limitation of the Schmid–Leiman transformation is that there is no established method for determining the statistical significance of the transformed factor loadings. Gignac (2007) also highlighted that another limitation of the higher-order model is that complete mediation by lower order factors is assumed. It may not be realistic to assume that there are only indirect effects between the higher-order general factor and the observed variables.
A direct hierarchical model considers both direct and indirect effects among all latent and observed variables (Gustafsson & Balke, 1993). The benefits associated with the direct hierarchical modelling strategy include (a) non-negligibly higher levels of model fit, (b) statistical significance testing for all parameter estimates, and (c) less ambiguous interpretations of the factor loadings (Gignac, 2007). Gignac, 2005, Gignac, 2006 and Gignac, 2007) demonstrated the advantages of the nested factors model to more conventional oblique and high-order modelling approaches using well known intelligence and personality measures. A direct hierarchical model of the EQ-i is presented in Fig. 2.Many self-report measures in psychology include some items that are reverse worded and it is expected that reverse-worded items measure the same construct as positively worded or straightforwardly worded items. The purpose of including the reverse-worded items is to reduce the tendency for participants to agree more than disagree (acquiescence bias) or respond according to their general perceptions about the topic rather than the specific question (a response set; Woods, 2006). If responses differ due to the direction of the item wording, then the data may be confounded by a method effect caused by the mechanism used to collect the information (DiStefano & Motl, 2006). The factorial validity interpreted by a researcher may be compromised if method variance associated with item wording is correlated because the magnitude of the correlation between scale scores may be incorrect.
The EQ-i includes 49.23% of items that are reverse scored. DiStefano and Motl (2006) demonstrated that method effects associated with negatively or reverse-worded items on the Rosenberg Self-Esteem (RSE) scale (Rosenberg, 1989) may be considered a response style and should be tested for when investigating factorial validity. Negatively worded items could be estimated as a distinct latent variable when testing model fit. Gignac (2010) included a latent variable for negatively worded items (parcelled) in a nested factors model evaluating the dimensional structure of the GENOS EI measure. Woods (2006) used Monte Carlo simulation with samples of 1000, 500, and 250 to show that a two-factor model accounting for reverse-worded items in a 23-item scale was a good fit even when the percentage of reverse scored items were manipulated from 43% to 26% to 13%. The one factor model that did not account for reverse-worded items showed poorer fit as the number of reverse-worded items increased. Method effects associated with negatively worded or reverse-worded items included in the EQ-i have yet to be examined.
The EQ-i was used by Perlini and Halverson (2006) to predict performance outcomes in a professional sport sample including number of games played and points scored. To continue investigating the underpinning theory and predictive validity of the EQ-i in professional sport, the psychometric properties of the EQ-i should be confirmed in a sport sample. The purpose of this study was to confirm the 1-5-15 dimensional structure of the EQ-i (Bar-On, 1997) currently in use and available to applied practitioners and researchers in sport psychology in a sample of male athletes using direct hierarchical modelling. A re-specification of the EQ-i model presenting the best fit for the data was also conducted, according to Anderson and Gerbing’s (1988) recommendations, to further explore the validity of the EQ-i in a sport sample. Alternative models were not tested as there was no theoretical rationale to estimate a series of models according to Anderson & Gerbing’s two-step process. The 15 subscales were then independently examined at the item level to determine factorial validity while accounting for method effects associated with reverse scored items.

نتیجه گیری انگلیسی

he descriptive statistics and Cronbach α for the subscales are presented in Table 1. Cronbach α for the subscales ranged between .70 and .81. Data from Bar-On’s (1997) U.S. sample of males from the general population are also included as comparisons. Overall, the internal consistency of the subscales scores are adequate but lower than the Bar-On sample. Impulse control and self-regard show the highest level of internal consistency. The mean item correlations, covariances, and total item correlations are presented in Table 2. The mean item correlations for each of the 15 subscales ranged between .23 and 28 for all subscales except impulse control and self-regard, which had the highest scores with .31 and .32 respectively. The mean item covariation ranged between .13 and .25 for all subscales except for impulse control with .31.The data were screened for outliers and normality and were considered appropriately distributed for the purposes of maximum likelihood estimation. Curran, West, and Finch (1996) recommended that for CFA, variables with an absolute value greater than 2 for skewness or 7 for kurtosis should be transformed to improve the distribution of scores. No variables met the recommended criteria and hence no variables were transformed.
The direct hierarchical model for the 1-5-15 dimensional structure was a poor fit, χ2 = 1170.29 (df = 77, N = 700), p < .05, and SRMR = .083, RMSEA = .143, CFI = .790, TLI = .713. The factor loadings are presented in Table 3. The general factor was associated with factor loadings ranging in size from .30 to .79, which were significant. The factor loadings for the subscales self-regard and self-actualisation were not significant. Self-actualisation and optimism had differentially directed loadings (see Table 3), suggesting the subscales were not associated with sufficient unique covariation when related to the composite scale. An additional five subscales showed factor loadings of less than .30 to the representative composite scale. Overall, the results demonstrated that the 1-5-15 dimensional structure of the EQ-i as suggested by Bar-On (1997) was not stable. The re-specification of the model representing the best fit for the data was a 1-4-15 dimensional structure, χ2 = 524.12 (df = 67, N = 700), p < .05, and SRMR = .053, RMSEA = .099, CFI = .912, TLI = .862. The factor loadings are presented in Table 4 and demonstrated that the dimensional structure was also not stable due to the number of differentially directed loadings to composite scales.Of the 130 items directly contributing to subscale scores in the EQ-i, 64 (49.23%) items are reversed scored. DiStefano and Motl (2006) and Gignac (2010) assessed the effects of negatively worded items by including a nested factor to which negatively worded items are loaded. To further examine the factorial validity of the EQ-i subscales a direct hierarchical model of each subscale was conducted using CFA and included a latent variable to which reverse scored items loaded. The fit indices for the 1-5-15 model, best fit for data model, and 15 subscales analysed in the current study are included in Table 5.The subscales showed close fit (i.e., assertiveness, self-actualisation, reality testing, and problem solving), reasonably good fit (i.e., social responsibility, stress tolerance, flexibility, optimism) and mediocre fit (i.e., self-regard, emotional self-awareness, independence, empathy, interpersonal relationship). The impulse control and happiness subscales showed unacceptable fit and require further examination.